Overview

Dataset statistics

Number of variables22
Number of observations1440
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory258.8 KiB
Average record size in memory184.0 B

Variable types

Numeric17
DateTime1
Categorical4

Alerts

id is highly overall correlated with fuel_type_electric and 2 other fieldsHigh correlation
fuel_type_petrol is highly overall correlated with fuel_type_diesel and 10 other fieldsHigh correlation
fuel_type_diesel is highly overall correlated with fuel_type_petrol and 10 other fieldsHigh correlation
fuel_type_electric is highly overall correlated with id and 3 other fieldsHigh correlation
fuel_type_others is highly overall correlated with fuel_type_electric and 5 other fieldsHigh correlation
vehicleClass_MotorCycle is highly overall correlated with fuel_type_petrol and 10 other fieldsHigh correlation
vehicleClass_MotorCar is highly overall correlated with fuel_type_petrol and 12 other fieldsHigh correlation
vehicleClass_others is highly overall correlated with fuel_type_petrol and 10 other fieldsHigh correlation
seatCapacity_1_to_3 is highly overall correlated with fuel_type_petrol and 10 other fieldsHigh correlation
seatCapacity_4_to_6 is highly overall correlated with fuel_type_petrol and 11 other fieldsHigh correlation
seatCapacity_above_6 is highly overall correlated with fuel_type_petrol and 11 other fieldsHigh correlation
brand_new_vehicles is highly overall correlated with fuel_type_petrol and 10 other fieldsHigh correlation
pre_owned_vehicles is highly overall correlated with fuel_type_petrol and 12 other fieldsHigh correlation
category_non_transport is highly overall correlated with fuel_type_petrol and 10 other fieldsHigh correlation
category_transport is highly overall correlated with fuel_type_petrol and 11 other fieldsHigh correlation
mmm is highly overall correlated with quarterHigh correlation
quarter is highly overall correlated with id and 1 other fieldsHigh correlation
fiscal_year is highly overall correlated with idHigh correlation
id is uniformly distributedUniform
district is uniformly distributedUniform
mmm is uniformly distributedUniform
quarter is uniformly distributedUniform
fiscal_year is uniformly distributedUniform
id has unique valuesUnique
fuel_type_electric has 344 (23.9%) zerosZeros
fuel_type_others has 330 (22.9%) zerosZeros
vehicleClass_MotorCar has 27 (1.9%) zerosZeros
vehicleClass_AutoRickshaw has 94 (6.5%) zerosZeros
vehicleClass_Agriculture has 25 (1.7%) zerosZeros
seatCapacity_4_to_6 has 25 (1.7%) zerosZeros
seatCapacity_above_6 has 28 (1.9%) zerosZeros
pre_owned_vehicles has 24 (1.7%) zerosZeros

Reproduction

Analysis started2023-09-16 01:21:48.229983
Analysis finished2023-09-16 01:22:39.493829
Duration51.26 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1440
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean720.5
Minimum1
Maximum1440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:39.625754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile72.95
Q1360.75
median720.5
Q31080.25
95-th percentile1368.05
Maximum1440
Range1439
Interquartile range (IQR)719.5

Descriptive statistics

Standard deviation415.83651
Coefficient of variation (CV)0.5771499
Kurtosis-1.2
Mean720.5
Median Absolute Deviation (MAD)360
Skewness0
Sum1037520
Variance172920
MonotonicityStrictly increasing
2023-09-16T02:22:39.790672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
968 1
 
0.1%
966 1
 
0.1%
965 1
 
0.1%
964 1
 
0.1%
963 1
 
0.1%
962 1
 
0.1%
961 1
 
0.1%
960 1
 
0.1%
959 1
 
0.1%
Other values (1430) 1430
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1440 1
0.1%
1439 1
0.1%
1438 1
0.1%
1437 1
0.1%
1436 1
0.1%
1435 1
0.1%
1434 1
0.1%
1433 1
0.1%
1432 1
0.1%
1431 1
0.1%

month
Date

Distinct48
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
Minimum2019-04-01 00:00:00
Maximum2023-03-01 00:00:00
2023-09-16T02:22:39.964573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:40.151452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

fuel_type_petrol
Real number (ℝ)

Distinct1251
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3835.6007
Minimum0
Maximum39689
Zeros10
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:40.342341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile643.05
Q11346.25
median2067.5
Q33412
95-th percentile18272.45
Maximum39689
Range39689
Interquartile range (IQR)2065.75

Descriptive statistics

Standard deviation5245.7898
Coefficient of variation (CV)1.367658
Kurtosis8.019446
Mean3835.6007
Median Absolute Deviation (MAD)918
Skewness2.8652969
Sum5523265
Variance27518311
MonotonicityNot monotonic
2023-09-16T02:22:40.520252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
0.7%
4 5
 
0.3%
1260 4
 
0.3%
1668 3
 
0.2%
452 3
 
0.2%
1772 3
 
0.2%
2267 3
 
0.2%
1196 3
 
0.2%
1137 3
 
0.2%
1021 3
 
0.2%
Other values (1241) 1400
97.2%
ValueCountFrequency (%)
0 10
0.7%
2 2
 
0.1%
3 1
 
0.1%
4 5
0.3%
6 2
 
0.1%
8 2
 
0.1%
12 1
 
0.1%
14 1
 
0.1%
20 1
 
0.1%
31 1
 
0.1%
ValueCountFrequency (%)
39689 1
0.1%
32104 1
0.1%
31646 1
0.1%
28578 1
0.1%
26996 1
0.1%
26643 1
0.1%
26435 1
0.1%
26280 1
0.1%
26093 1
0.1%
25950 1
0.1%

fuel_type_diesel
Real number (ℝ)

Distinct850
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.29444
Minimum0
Maximum5270
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:40.692727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile132.9
Q1261.75
median445.5
Q3744
95-th percentile2234.1
Maximum5270
Range5270
Interquartile range (IQR)482.25

Descriptive statistics

Standard deviation684.8239
Coefficient of variation (CV)1.0387224
Kurtosis8.4759592
Mean659.29444
Median Absolute Deviation (MAD)208.5
Skewness2.6851802
Sum949384
Variance468983.78
MonotonicityNot monotonic
2023-09-16T02:22:40.855617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 7
 
0.5%
155 6
 
0.4%
268 5
 
0.3%
518 5
 
0.3%
253 5
 
0.3%
549 5
 
0.3%
190 5
 
0.3%
163 5
 
0.3%
121 5
 
0.3%
249 5
 
0.3%
Other values (840) 1387
96.3%
ValueCountFrequency (%)
0 1
0.1%
6 1
0.1%
15 2
0.1%
19 1
0.1%
25 1
0.1%
30 2
0.1%
32 1
0.1%
34 1
0.1%
35 2
0.1%
36 2
0.1%
ValueCountFrequency (%)
5270 1
0.1%
4665 1
0.1%
4576 1
0.1%
4336 1
0.1%
4130 1
0.1%
4059 1
0.1%
3961 1
0.1%
3842 1
0.1%
3728 1
0.1%
3695 1
0.1%

fuel_type_electric
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct221
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.985417
Minimum0
Maximum2782
Zeros344
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:41.019523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q335
95-th percentile303.1
Maximum2782
Range2782
Interquartile range (IQR)33

Descriptive statistics

Standard deviation247.5996
Coefficient of variation (CV)3.43958
Kurtosis42.403048
Mean71.985417
Median Absolute Deviation (MAD)8
Skewness5.9992088
Sum103659
Variance61305.561
MonotonicityNot monotonic
2023-09-16T02:22:41.170450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 344
23.9%
2 159
 
11.0%
4 85
 
5.9%
6 65
 
4.5%
8 44
 
3.1%
12 26
 
1.8%
10 24
 
1.7%
16 21
 
1.5%
14 18
 
1.2%
20 18
 
1.2%
Other values (211) 636
44.2%
ValueCountFrequency (%)
0 344
23.9%
1 12
 
0.8%
2 159
11.0%
3 11
 
0.8%
4 85
 
5.9%
5 11
 
0.8%
6 65
 
4.5%
7 16
 
1.1%
8 44
 
3.1%
9 12
 
0.8%
ValueCountFrequency (%)
2782 1
0.1%
2606 1
0.1%
2304 1
0.1%
2237 1
0.1%
2144 1
0.1%
2003 1
0.1%
1628 1
0.1%
1605 1
0.1%
1599 1
0.1%
1582 1
0.1%

fuel_type_others
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct243
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.13125
Minimum0
Maximum2326
Zeros330
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:41.343353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q334
95-th percentile252.05
Maximum2326
Range2326
Interquartile range (IQR)32

Descriptive statistics

Standard deviation204.99126
Coefficient of variation (CV)3.2993262
Kurtosis49.316748
Mean62.13125
Median Absolute Deviation (MAD)6
Skewness6.5038766
Sum89469
Variance42021.418
MonotonicityNot monotonic
2023-09-16T02:22:41.504246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 330
22.9%
2 177
 
12.3%
4 100
 
6.9%
6 67
 
4.7%
8 47
 
3.3%
10 43
 
3.0%
12 23
 
1.6%
14 22
 
1.5%
5 21
 
1.5%
16 20
 
1.4%
Other values (233) 590
41.0%
ValueCountFrequency (%)
0 330
22.9%
1 20
 
1.4%
2 177
12.3%
3 15
 
1.0%
4 100
 
6.9%
5 21
 
1.5%
6 67
 
4.7%
7 14
 
1.0%
8 47
 
3.3%
9 16
 
1.1%
ValueCountFrequency (%)
2326 1
0.1%
2086 1
0.1%
2042 1
0.1%
1848 1
0.1%
1755 1
0.1%
1696 1
0.1%
1652 1
0.1%
1636 1
0.1%
1547 1
0.1%
1540 1
0.1%

vehicleClass_MotorCycle
Real number (ℝ)

Distinct1203
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3340.2688
Minimum0
Maximum35420
Zeros10
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:41.668186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile540.45
Q11229.75
median1887
Q33149.25
95-th percentile15030.55
Maximum35420
Range35420
Interquartile range (IQR)1919.5

Descriptive statistics

Standard deviation4349.4062
Coefficient of variation (CV)1.3021126
Kurtosis9.240943
Mean3340.2688
Median Absolute Deviation (MAD)822
Skewness2.9655482
Sum4809987
Variance18917334
MonotonicityNot monotonic
2023-09-16T02:22:41.831092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
0.7%
965 5
 
0.3%
2 4
 
0.3%
1223 4
 
0.3%
1707 4
 
0.3%
2632 4
 
0.3%
1532 4
 
0.3%
2308 3
 
0.2%
1103 3
 
0.2%
1555 3
 
0.2%
Other values (1193) 1396
96.9%
ValueCountFrequency (%)
0 10
0.7%
2 4
 
0.3%
3 1
 
0.1%
4 3
 
0.2%
6 2
 
0.1%
8 2
 
0.1%
12 1
 
0.1%
14 1
 
0.1%
20 1
 
0.1%
31 1
 
0.1%
ValueCountFrequency (%)
35420 1
0.1%
27869 1
0.1%
25563 1
0.1%
25527 1
0.1%
24431 1
0.1%
24328 1
0.1%
24285 1
0.1%
22699 1
0.1%
22585 1
0.1%
22499 1
0.1%

vehicleClass_MotorCar
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct676
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean719.74306
Minimum0
Maximum8036
Zeros27
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:42.003998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1123
median228.5
Q3432.5
95-th percentile4650.85
Maximum8036
Range8036
Interquartile range (IQR)309.5

Descriptive statistics

Standard deviation1455.4873
Coefficient of variation (CV)2.0222318
Kurtosis8.5822349
Mean719.74306
Median Absolute Deviation (MAD)126
Skewness3.0599784
Sum1036430
Variance2118443.2
MonotonicityNot monotonic
2023-09-16T02:22:42.174907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
1.9%
113 10
 
0.7%
106 9
 
0.6%
130 8
 
0.6%
99 8
 
0.6%
52 7
 
0.5%
84 7
 
0.5%
123 7
 
0.5%
231 7
 
0.5%
140 7
 
0.5%
Other values (666) 1343
93.3%
ValueCountFrequency (%)
0 27
1.9%
2 3
 
0.2%
16 1
 
0.1%
17 1
 
0.1%
20 3
 
0.2%
21 1
 
0.1%
22 2
 
0.1%
27 3
 
0.2%
30 3
 
0.2%
32 1
 
0.1%
ValueCountFrequency (%)
8036 1
0.1%
7811 1
0.1%
7780 1
0.1%
7696 1
0.1%
7617 1
0.1%
7607 1
0.1%
7492 1
0.1%
7447 1
0.1%
7383 1
0.1%
7359 1
0.1%

vehicleClass_AutoRickshaw
Real number (ℝ)

Distinct303
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.850694
Minimum0
Maximum2287
Zeros94
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:42.357802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116
median36
Q390
95-th percentile331.4
Maximum2287
Range2287
Interquartile range (IQR)74

Descriptive statistics

Standard deviation197.17876
Coefficient of variation (CV)2.1009835
Kurtosis44.843973
Mean93.850694
Median Absolute Deviation (MAD)28
Skewness5.9307487
Sum135145
Variance38879.463
MonotonicityNot monotonic
2023-09-16T02:22:42.520722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
 
6.5%
8 37
 
2.6%
22 31
 
2.2%
10 31
 
2.2%
16 29
 
2.0%
12 29
 
2.0%
6 29
 
2.0%
20 28
 
1.9%
4 21
 
1.5%
32 21
 
1.5%
Other values (293) 1090
75.7%
ValueCountFrequency (%)
0 94
6.5%
1 8
 
0.6%
2 18
 
1.2%
3 3
 
0.2%
4 21
 
1.5%
5 5
 
0.3%
6 29
 
2.0%
7 10
 
0.7%
8 37
 
2.6%
9 9
 
0.6%
ValueCountFrequency (%)
2287 1
0.1%
2041 1
0.1%
2006 1
0.1%
1705 1
0.1%
1684 1
0.1%
1640 1
0.1%
1622 1
0.1%
1614 1
0.1%
1518 1
0.1%
1509 1
0.1%

vehicleClass_Agriculture
Real number (ℝ)

Distinct381
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.9125
Minimum0
Maximum786
Zeros25
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:42.687269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.95
Q149
median105
Q3189
95-th percentile359.3
Maximum786
Range786
Interquartile range (IQR)140

Descriptive statistics

Standard deviation118.48606
Coefficient of variation (CV)0.87178194
Kurtosis3.4806257
Mean135.9125
Median Absolute Deviation (MAD)65
Skewness1.5954133
Sum195714
Variance14038.947
MonotonicityNot monotonic
2023-09-16T02:22:42.860164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
1.7%
2 19
 
1.3%
46 12
 
0.8%
6 12
 
0.8%
40 11
 
0.8%
56 11
 
0.8%
16 11
 
0.8%
12 11
 
0.8%
100 11
 
0.8%
18 10
 
0.7%
Other values (371) 1307
90.8%
ValueCountFrequency (%)
0 25
1.7%
2 19
1.3%
3 5
 
0.3%
4 8
 
0.6%
5 3
 
0.2%
6 12
0.8%
7 5
 
0.3%
8 10
 
0.7%
9 2
 
0.1%
10 6
 
0.4%
ValueCountFrequency (%)
786 1
0.1%
760 1
0.1%
745 1
0.1%
686 1
0.1%
673 1
0.1%
654 1
0.1%
623 1
0.1%
622 1
0.1%
614 1
0.1%
599 1
0.1%

vehicleClass_others
Real number (ℝ)

Distinct578
Distinct (%)40.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean339.32778
Minimum0
Maximum4299
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:43.039077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60.95
Q1122
median199
Q3332.25
95-th percentile1322.2
Maximum4299
Range4299
Interquartile range (IQR)210.25

Descriptive statistics

Standard deviation449.46776
Coefficient of variation (CV)1.3245829
Kurtosis16.86754
Mean339.32778
Median Absolute Deviation (MAD)95
Skewness3.6465957
Sum488632
Variance202021.27
MonotonicityNot monotonic
2023-09-16T02:22:43.193020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 11
 
0.8%
147 10
 
0.7%
178 9
 
0.6%
152 9
 
0.6%
177 9
 
0.6%
160 9
 
0.6%
144 8
 
0.6%
161 8
 
0.6%
74 8
 
0.6%
205 8
 
0.6%
Other values (568) 1351
93.8%
ValueCountFrequency (%)
0 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
6 2
0.1%
7 3
0.2%
8 1
 
0.1%
9 2
0.1%
10 1
 
0.1%
12 4
0.3%
14 3
0.2%
ValueCountFrequency (%)
4299 1
0.1%
3749 1
0.1%
3642 1
0.1%
3276 1
0.1%
3272 1
0.1%
3116 1
0.1%
2647 1
0.1%
2590 1
0.1%
2530 1
0.1%
2443 1
0.1%

seatCapacity_1_to_3
Real number (ℝ)

Distinct1255
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3750.5167
Minimum6
Maximum36694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:43.360475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile670.25
Q11486.25
median2244.5
Q33635.5
95-th percentile16261.2
Maximum36694
Range36688
Interquartile range (IQR)2149.25

Descriptive statistics

Standard deviation4609.4072
Coefficient of variation (CV)1.2290059
Kurtosis8.6603665
Mean3750.5167
Median Absolute Deviation (MAD)963.5
Skewness2.8827111
Sum5400744
Variance21246635
MonotonicityNot monotonic
2023-09-16T02:22:43.529394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1065 4
 
0.3%
1318 4
 
0.3%
3827 4
 
0.3%
868 4
 
0.3%
1656 3
 
0.2%
3385 3
 
0.2%
1789 3
 
0.2%
1596 3
 
0.2%
1388 3
 
0.2%
1516 3
 
0.2%
Other values (1245) 1406
97.6%
ValueCountFrequency (%)
6 1
0.1%
15 1
0.1%
18 1
0.1%
19 1
0.1%
23 1
0.1%
25 1
0.1%
32 2
0.1%
34 1
0.1%
36 2
0.1%
38 1
0.1%
ValueCountFrequency (%)
36694 1
0.1%
29579 1
0.1%
28023 1
0.1%
27197 1
0.1%
25359 1
0.1%
25250 1
0.1%
25215 1
0.1%
23539 1
0.1%
23524 1
0.1%
23513 1
0.1%

seatCapacity_4_to_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct735
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean768.71528
Minimum0
Maximum7918
Zeros25
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:43.704278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68
Q1154
median273
Q3543.5
95-th percentile4706.25
Maximum7918
Range7918
Interquartile range (IQR)389.5

Descriptive statistics

Standard deviation1413.9333
Coefficient of variation (CV)1.8393459
Kurtosis7.9570332
Mean768.71528
Median Absolute Deviation (MAD)145
Skewness2.9600651
Sum1106950
Variance1999207.4
MonotonicityNot monotonic
2023-09-16T02:22:43.870181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
1.7%
145 12
 
0.8%
179 9
 
0.6%
204 9
 
0.6%
116 9
 
0.6%
144 8
 
0.6%
142 8
 
0.6%
245 7
 
0.5%
220 7
 
0.5%
114 7
 
0.5%
Other values (725) 1339
93.0%
ValueCountFrequency (%)
0 25
1.7%
1 1
 
0.1%
2 4
 
0.3%
21 1
 
0.1%
26 3
 
0.2%
28 1
 
0.1%
30 2
 
0.1%
33 1
 
0.1%
42 1
 
0.1%
43 1
 
0.1%
ValueCountFrequency (%)
7918 1
0.1%
7568 1
0.1%
7496 1
0.1%
7434 1
0.1%
7325 1
0.1%
7201 1
0.1%
7158 1
0.1%
7139 1
0.1%
7032 1
0.1%
6983 1
0.1%

seatCapacity_above_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct290
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.19028
Minimum0
Maximum1337
Zeros28
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:44.040085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q120
median38
Q375.25
95-th percentile682.1
Maximum1337
Range1337
Interquartile range (IQR)55.25

Descriptive statistics

Standard deviation209.77188
Coefficient of variation (CV)1.9389162
Kurtosis9.8343478
Mean108.19028
Median Absolute Deviation (MAD)22
Skewness3.1828708
Sum155794
Variance44004.242
MonotonicityNot monotonic
2023-09-16T02:22:44.214043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 37
 
2.6%
18 33
 
2.3%
22 32
 
2.2%
14 32
 
2.2%
8 30
 
2.1%
0 28
 
1.9%
32 27
 
1.9%
24 26
 
1.8%
16 24
 
1.7%
11 24
 
1.7%
Other values (280) 1147
79.7%
ValueCountFrequency (%)
0 28
1.9%
1 2
 
0.1%
2 6
 
0.4%
3 3
 
0.2%
4 8
 
0.6%
5 8
 
0.6%
6 16
1.1%
7 9
 
0.6%
8 30
2.1%
9 12
 
0.8%
ValueCountFrequency (%)
1337 1
0.1%
1237 1
0.1%
1232 1
0.1%
1221 1
0.1%
1220 1
0.1%
1217 1
0.1%
1120 1
0.1%
1111 1
0.1%
1093 1
0.1%
1085 1
0.1%

brand_new_vehicles
Real number (ℝ)

Distinct1276
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4317.7785
Minimum6
Maximum42073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:44.397515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile762.8
Q11629.75
median2457
Q34095.25
95-th percentile19700.2
Maximum42073
Range42067
Interquartile range (IQR)2465.5

Descriptive statistics

Standard deviation5531.4115
Coefficient of variation (CV)1.2810781
Kurtosis8.0067854
Mean4317.7785
Median Absolute Deviation (MAD)1072
Skewness2.8330494
Sum6217601
Variance30596514
MonotonicityNot monotonic
2023-09-16T02:22:44.561420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1481 4
 
0.3%
2903 4
 
0.3%
1992 3
 
0.2%
2217 3
 
0.2%
2545 3
 
0.2%
2581 3
 
0.2%
3230 3
 
0.2%
1194 3
 
0.2%
3276 3
 
0.2%
2361 3
 
0.2%
Other values (1266) 1408
97.8%
ValueCountFrequency (%)
6 1
0.1%
15 1
0.1%
18 1
0.1%
19 1
0.1%
23 1
0.1%
25 1
0.1%
30 1
0.1%
34 2
0.1%
36 2
0.1%
38 1
0.1%
ValueCountFrequency (%)
42073 1
0.1%
35033 1
0.1%
33786 1
0.1%
31319 1
0.1%
29401 1
0.1%
29328 1
0.1%
28828 1
0.1%
27738 1
0.1%
27121 1
0.1%
26560 1
0.1%

pre_owned_vehicles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct428
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.34306
Minimum0
Maximum4450
Zeros24
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:44.729338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q157
median101
Q3167
95-th percentile2097.15
Maximum4450
Range4450
Interquartile range (IQR)110

Descriptive statistics

Standard deviation648.26039
Coefficient of variation (CV)2.0821418
Kurtosis9.7209792
Mean311.34306
Median Absolute Deviation (MAD)49
Skewness3.1739966
Sum448334
Variance420241.53
MonotonicityNot monotonic
2023-09-16T02:22:44.886233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
1.7%
52 17
 
1.2%
88 17
 
1.2%
66 16
 
1.1%
46 15
 
1.0%
57 15
 
1.0%
58 14
 
1.0%
72 14
 
1.0%
38 14
 
1.0%
49 14
 
1.0%
Other values (418) 1280
88.9%
ValueCountFrequency (%)
0 24
1.7%
2 5
 
0.3%
4 2
 
0.1%
8 1
 
0.1%
10 2
 
0.1%
12 3
 
0.2%
13 1
 
0.1%
14 2
 
0.1%
15 4
 
0.3%
16 3
 
0.2%
ValueCountFrequency (%)
4450 1
0.1%
4193 1
0.1%
3912 1
0.1%
3750 1
0.1%
3726 1
0.1%
3701 1
0.1%
3593 1
0.1%
3404 1
0.1%
3320 1
0.1%
3265 1
0.1%

category_non_transport
Real number (ℝ)

Distinct1243
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4213.1049
Minimum6
Maximum42074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:45.059141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile700.25
Q11497
median2295
Q33783.25
95-th percentile20101.75
Maximum42074
Range42068
Interquartile range (IQR)2286.25

Descriptive statistics

Standard deviation5693.8004
Coefficient of variation (CV)1.35145
Kurtosis7.7181745
Mean4213.1049
Median Absolute Deviation (MAD)1004
Skewness2.8321689
Sum6066871
Variance32419363
MonotonicityNot monotonic
2023-09-16T02:22:45.219052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1746 4
 
0.3%
1167 4
 
0.3%
1043 4
 
0.3%
1733 4
 
0.3%
1044 3
 
0.2%
2005 3
 
0.2%
1937 3
 
0.2%
1363 3
 
0.2%
37 3
 
0.2%
1431 3
 
0.2%
Other values (1233) 1406
97.6%
ValueCountFrequency (%)
6 1
0.1%
10 1
0.1%
12 1
0.1%
13 1
0.1%
14 1
0.1%
16 1
0.1%
18 2
0.1%
22 1
0.1%
24 1
0.1%
27 1
0.1%
ValueCountFrequency (%)
42074 1
0.1%
35218 1
0.1%
33305 1
0.1%
29698 1
0.1%
28769 1
0.1%
28247 1
0.1%
27989 1
0.1%
27976 1
0.1%
27809 1
0.1%
27421 1
0.1%

category_transport
Real number (ℝ)

Distinct680
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.01667
Minimum0
Maximum4239
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-09-16T02:22:45.391490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile71.9
Q1149
median250
Q3449.25
95-th percentile1409.5
Maximum4239
Range4239
Interquartile range (IQR)300.25

Descriptive statistics

Standard deviation525.92418
Coefficient of variation (CV)1.2641902
Kurtosis15.150698
Mean416.01667
Median Absolute Deviation (MAD)125
Skewness3.5218773
Sum599064
Variance276596.24
MonotonicityNot monotonic
2023-09-16T02:22:45.551395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157 10
 
0.7%
120 10
 
0.7%
197 9
 
0.6%
225 8
 
0.6%
107 8
 
0.6%
284 8
 
0.6%
180 7
 
0.5%
168 7
 
0.5%
84 7
 
0.5%
203 7
 
0.5%
Other values (670) 1359
94.4%
ValueCountFrequency (%)
0 2
0.1%
1 1
 
0.1%
4 2
0.1%
5 1
 
0.1%
7 2
0.1%
8 2
0.1%
9 2
0.1%
10 3
0.2%
12 3
0.2%
13 1
 
0.1%
ValueCountFrequency (%)
4239 1
0.1%
3844 1
0.1%
3811 1
0.1%
3804 1
0.1%
3788 1
0.1%
3666 1
0.1%
3565 1
0.1%
3441 1
0.1%
3403 1
0.1%
3314 1
0.1%

district
Categorical

Distinct30
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
Rangareddy
 
48
Kamareddy
 
48
Rajanna Sircilla
 
48
Jangoan
 
48
Jayashankar Bhupalpally
 
48
Other values (25)
1200 

Length

Max length23
Median length19.5
Mean length11.2
Min length5

Characters and Unicode

Total characters16128
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRangareddy
2nd rowKamareddy
3rd rowRajanna Sircilla
4th rowJangoan
5th rowJayashankar Bhupalpally

Common Values

ValueCountFrequency (%)
Rangareddy 48
 
3.3%
Kamareddy 48
 
3.3%
Rajanna Sircilla 48
 
3.3%
Jangoan 48
 
3.3%
Jayashankar Bhupalpally 48
 
3.3%
Mahabubnagar 48
 
3.3%
Siddipet 48
 
3.3%
Nagarkurnool 48
 
3.3%
Nirmal 48
 
3.3%
Jogulamba Gadwal 48
 
3.3%
Other values (20) 960
66.7%

Length

2023-09-16T02:22:45.707320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rangareddy 48
 
2.8%
kamareddy 48
 
2.8%
bhuvanagiri 48
 
2.8%
karimnagar 48
 
2.8%
hyderabad 48
 
2.8%
medchal_malkajgiri 48
 
2.8%
sangareddy 48
 
2.8%
medak 48
 
2.8%
nizamabad 48
 
2.8%
wanaparthy 48
 
2.8%
Other values (26) 1248
72.2%

Most occurring characters

ValueCountFrequency (%)
a 3696
22.9%
d 1296
 
8.0%
r 1104
 
6.8%
i 912
 
5.7%
l 864
 
5.4%
n 720
 
4.5%
g 624
 
3.9%
e 624
 
3.9%
m 576
 
3.6%
b 576
 
3.6%
Other values (30) 5136
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14016
86.9%
Uppercase Letter 1776
 
11.0%
Space Separator 288
 
1.8%
Connector Punctuation 48
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3696
26.4%
d 1296
 
9.2%
r 1104
 
7.9%
i 912
 
6.5%
l 864
 
6.2%
n 720
 
5.1%
g 624
 
4.5%
e 624
 
4.5%
m 576
 
4.1%
b 576
 
4.1%
Other values (14) 3024
21.6%
Uppercase Letter
ValueCountFrequency (%)
M 288
16.2%
K 240
13.5%
J 192
10.8%
N 192
10.8%
S 192
10.8%
B 144
8.1%
W 96
 
5.4%
A 96
 
5.4%
R 96
 
5.4%
G 48
 
2.7%
Other values (4) 192
10.8%
Space Separator
ValueCountFrequency (%)
288
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15792
97.9%
Common 336
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3696
23.4%
d 1296
 
8.2%
r 1104
 
7.0%
i 912
 
5.8%
l 864
 
5.5%
n 720
 
4.6%
g 624
 
4.0%
e 624
 
4.0%
m 576
 
3.6%
b 576
 
3.6%
Other values (28) 4800
30.4%
Common
ValueCountFrequency (%)
288
85.7%
_ 48
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3696
22.9%
d 1296
 
8.0%
r 1104
 
6.8%
i 912
 
5.7%
l 864
 
5.4%
n 720
 
4.5%
g 624
 
3.9%
e 624
 
3.9%
m 576
 
3.6%
b 576
 
3.6%
Other values (30) 5136
31.8%

mmm
Categorical

HIGH CORRELATION  UNIFORM 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
Apr
120 
May
120 
Jun
120 
Jul
120 
Aug
120 
Other values (7)
840 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4320
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApr
2nd rowApr
3rd rowApr
4th rowApr
5th rowApr

Common Values

ValueCountFrequency (%)
Apr 120
8.3%
May 120
8.3%
Jun 120
8.3%
Jul 120
8.3%
Aug 120
8.3%
Sep 120
8.3%
Oct 120
8.3%
Nov 120
8.3%
Dec 120
8.3%
Jan 120
8.3%
Other values (2) 240
16.7%

Length

2023-09-16T02:22:45.845227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apr 120
8.3%
may 120
8.3%
jun 120
8.3%
jul 120
8.3%
aug 120
8.3%
sep 120
8.3%
oct 120
8.3%
nov 120
8.3%
dec 120
8.3%
jan 120
8.3%
Other values (2) 240
16.7%

Most occurring characters

ValueCountFrequency (%)
a 360
 
8.3%
J 360
 
8.3%
u 360
 
8.3%
e 360
 
8.3%
A 240
 
5.6%
r 240
 
5.6%
M 240
 
5.6%
n 240
 
5.6%
p 240
 
5.6%
c 240
 
5.6%
Other values (12) 1440
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2880
66.7%
Uppercase Letter 1440
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 360
12.5%
u 360
12.5%
e 360
12.5%
r 240
8.3%
n 240
8.3%
p 240
8.3%
c 240
8.3%
v 120
 
4.2%
o 120
 
4.2%
t 120
 
4.2%
Other values (4) 480
16.7%
Uppercase Letter
ValueCountFrequency (%)
J 360
25.0%
A 240
16.7%
M 240
16.7%
N 120
 
8.3%
F 120
 
8.3%
D 120
 
8.3%
S 120
 
8.3%
O 120
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4320
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 360
 
8.3%
J 360
 
8.3%
u 360
 
8.3%
e 360
 
8.3%
A 240
 
5.6%
r 240
 
5.6%
M 240
 
5.6%
n 240
 
5.6%
p 240
 
5.6%
c 240
 
5.6%
Other values (12) 1440
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 360
 
8.3%
J 360
 
8.3%
u 360
 
8.3%
e 360
 
8.3%
A 240
 
5.6%
r 240
 
5.6%
M 240
 
5.6%
n 240
 
5.6%
p 240
 
5.6%
c 240
 
5.6%
Other values (12) 1440
33.3%

quarter
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
Q1
360 
Q2
360 
Q3
360 
Q4
360 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2880
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q1 360
25.0%
Q2 360
25.0%
Q3 360
25.0%
Q4 360
25.0%

Length

2023-09-16T02:22:45.963190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T02:22:46.111876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
q1 360
25.0%
q2 360
25.0%
q3 360
25.0%
q4 360
25.0%

Most occurring characters

ValueCountFrequency (%)
Q 1440
50.0%
1 360
 
12.5%
2 360
 
12.5%
3 360
 
12.5%
4 360
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1440
50.0%
Decimal Number 1440
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 360
25.0%
2 360
25.0%
3 360
25.0%
4 360
25.0%
Uppercase Letter
ValueCountFrequency (%)
Q 1440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1440
50.0%
Common 1440
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 360
25.0%
2 360
25.0%
3 360
25.0%
4 360
25.0%
Latin
ValueCountFrequency (%)
Q 1440
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Q 1440
50.0%
1 360
 
12.5%
2 360
 
12.5%
3 360
 
12.5%
4 360
 
12.5%

fiscal_year
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2019
360 
2020
360 
2021
360 
2022
360 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 360
25.0%
2020 360
25.0%
2021 360
25.0%
2022 360
25.0%

Length

2023-09-16T02:22:46.239789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T02:22:46.375709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2019 360
25.0%
2020 360
25.0%
2021 360
25.0%
2022 360
25.0%

Most occurring characters

ValueCountFrequency (%)
2 2880
50.0%
0 1800
31.2%
1 720
 
12.5%
9 360
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5760
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2880
50.0%
0 1800
31.2%
1 720
 
12.5%
9 360
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5760
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2880
50.0%
0 1800
31.2%
1 720
 
12.5%
9 360
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2880
50.0%
0 1800
31.2%
1 720
 
12.5%
9 360
 
6.2%

Interactions

2023-09-16T02:22:35.996139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:51.307591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:54.603946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:57.951452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:00.674174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:03.558669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:05.897555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:09.058761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:12.612667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:15.687891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:18.558237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:20.914878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:23.444433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:26.339371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:28.910764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:31.320324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.681167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:36.136060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:51.520468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:54.823820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:58.111362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:00.816059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:03.697575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:06.043458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:09.461528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:12.797559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:15.858795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:18.734138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:21.061793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:23.591335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:26.481287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:29.045871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:31.465249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.817117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:36.289971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:51.699365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:55.043693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:58.313803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:00.981966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:03.845490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:06.196368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:09.806202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:13.018432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:16.057679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:18.879052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:21.214705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:23.748258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:26.630203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:29.197275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:31.613157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.958011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:36.424780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:51.845836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:55.307541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:58.466714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:01.116885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:03.975417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:06.345286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:10.036143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:13.209322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:16.202609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.010978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:21.367619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:23.889163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:26.766109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:29.337195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:31.743080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:34.092968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:36.553700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:52.003744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:55.498430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:58.611630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:01.297781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:04.103358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:06.484233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:10.279003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:13.416202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:16.351511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.141914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:21.507536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:24.028082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:26.905318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:29.469128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:31.871410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:34.222974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:36.681976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:52.143670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:55.657036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:58.753549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:01.424709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:04.235301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:06.618194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:10.506871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:13.584106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:16.487432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.277836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:21.642468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:24.173013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:27.074224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:29.600166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:32.000321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:34.356367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:36.820884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:52.387524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:55.840929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:58.911459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:01.771509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:04.377820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:06.765113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:10.680772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:13.774994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:16.644355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.421739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:21.800382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:24.332907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:27.226135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:29.741088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:32.137260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:34.497273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:37.413554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:52.614393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:56.016837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:59.081995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:01.944408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:04.521725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:06.942598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:10.844676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:13.929905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:16.801250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.573651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:21.955280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:24.486832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:27.397128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:29.885037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:32.334133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:34.641202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:37.541440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:52.763630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:56.314655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:59.220915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:02.081897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:04.648650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:07.264409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:10.991591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:14.073823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:16.943170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.704576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:22.092198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:24.635732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:27.544188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:30.044380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:32.462031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:34.775112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:37.684463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:52.938529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:56.475567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:59.406808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:02.231809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:04.790568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:07.476303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:11.207468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:14.229734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:17.391910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.845495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:22.256107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:24.788658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:27.709107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:30.191308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:32.606968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:34.926351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:37.812404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:53.083446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:56.619478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:59.568714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:02.429697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:04.914499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:07.635201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:11.370374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:14.403633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:17.528835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:19.976419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:22.397023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:24.928564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:27.849049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:30.327218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:32.732898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:35.053289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:37.951308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:53.321310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:56.793325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:59.795583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:02.680551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:05.056414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:07.796958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:11.535291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:14.609514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:17.680743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:20.121336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:22.550935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:25.079478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:28.003951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:30.476138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:32.876610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:35.195215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:38.095240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:53.509201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:56.963239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:59.976479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:02.843459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:05.198945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:08.228708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:11.703190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:14.797405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:17.839653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:20.272262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:22.704845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:25.240384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:28.161864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:30.632441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.022538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:35.349119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:38.238143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:53.699674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:57.198886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:00.123394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:02.998368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:05.345874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:08.438588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:11.861100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:14.981299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:17.991566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:20.412168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:22.855785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:25.436285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:28.344762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:30.780370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.160446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:35.486433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:38.369069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:53.899556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:57.426754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:00.262534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:03.132900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:05.478781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:08.572510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:12.026003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:15.153200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:18.129484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:20.538109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:23.018665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:25.580448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:28.493988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:30.914280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.307374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:35.612362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:38.498008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:54.107437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:57.616654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:00.398469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:03.277819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:05.645699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:08.711444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:12.209898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:15.358083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:18.274403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:20.664023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:23.160596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:25.713360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:28.638919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:31.038486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.432302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:35.740287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:38.622922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:54.354294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:21:57.784550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:00.539376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:03.415737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:05.774612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:08.896855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:12.406785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:15.535980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:18.419317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:20.791948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:23.307512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:25.849281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:28.773845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:31.167425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:33.556236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-09-16T02:22:35.868214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-09-16T02:22:46.520626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
idfuel_type_petrolfuel_type_dieselfuel_type_electricfuel_type_othersvehicleClass_MotorCyclevehicleClass_MotorCarvehicleClass_AutoRickshawvehicleClass_AgriculturevehicleClass_othersseatCapacity_1_to_3seatCapacity_4_to_6seatCapacity_above_6brand_new_vehiclespre_owned_vehiclescategory_non_transportcategory_transportdistrictmmmquarterfiscal_year
id1.000-0.294-0.2740.6200.406-0.3350.078-0.196-0.097-0.135-0.316-0.0390.022-0.2950.108-0.283-0.1860.0000.3760.5730.929
fuel_type_petrol-0.2941.0000.8630.2640.3880.9970.8360.2950.2300.7940.9930.8510.7850.9940.8130.9970.7810.4020.0460.0650.123
fuel_type_diesel-0.2740.8631.0000.2060.4100.8590.7590.3610.3550.9170.8980.8210.7790.9070.7610.8830.9150.3410.0550.0530.191
fuel_type_electric0.6200.2640.2061.0000.6640.2330.549-0.011-0.2360.2880.2340.4820.4940.2610.5520.2710.2770.2140.0410.0520.140
fuel_type_others0.4060.3880.4100.6641.0000.3580.6230.272-0.0760.4710.3690.6330.6230.4050.6340.3990.5030.2490.0000.0360.113
vehicleClass_MotorCycle-0.3350.9970.8590.2330.3581.0000.7990.3010.2230.7810.9940.8230.7590.9910.7860.9940.7730.4300.0320.0370.136
vehicleClass_MotorCar0.0780.8360.7590.5490.6230.7991.0000.2390.1540.7440.8070.9550.8840.8380.9060.8440.7230.4140.0000.0000.054
vehicleClass_AutoRickshaw-0.1960.2950.361-0.0110.2720.3010.2391.0000.1470.3220.2940.4220.3410.3220.2980.2930.4980.2520.0000.0000.189
vehicleClass_Agriculture-0.0970.2300.355-0.236-0.0760.2230.1540.1471.0000.1990.2730.1130.0850.2620.1410.2670.1270.2490.1090.0680.141
vehicleClass_others-0.1350.7940.9170.2880.4710.7810.7440.3220.1991.0000.8210.7930.7930.8340.7510.8000.9560.3280.0290.0600.126
seatCapacity_1_to_3-0.3160.9930.8980.2340.3690.9940.8070.2940.2730.8211.0000.8280.7690.9970.7960.9950.8020.4070.0270.0480.142
seatCapacity_4_to_6-0.0390.8510.8210.4820.6330.8230.9550.4220.1130.7930.8281.0000.8950.8650.9010.8560.8340.3850.0650.0780.057
seatCapacity_above_60.0220.7850.7790.4940.6230.7590.8840.3410.0850.7930.7690.8951.0000.8000.8630.7910.8030.3590.0720.0480.115
brand_new_vehicles-0.2950.9940.9070.2610.4050.9910.8380.3220.2620.8340.9970.8650.8001.0000.8170.9960.8250.3960.0650.0810.138
pre_owned_vehicles0.1080.8130.7610.5520.6340.7860.9060.2980.1410.7510.7960.9010.8630.8171.0000.8210.7480.3810.0240.0520.098
category_non_transport-0.2830.9970.8830.2710.3990.9940.8440.2930.2670.8000.9950.8560.7910.9960.8211.0000.7850.3980.0530.0500.108
category_transport-0.1860.7810.9150.2770.5030.7730.7230.4980.1270.9560.8020.8340.8030.8250.7480.7851.0000.3310.0320.0700.182
district0.0000.4020.3410.2140.2490.4300.4140.2520.2490.3280.4070.3850.3590.3960.3810.3980.3311.0000.0000.0000.000
mmm0.3760.0460.0550.0410.0000.0320.0000.0000.1090.0290.0270.0650.0720.0650.0240.0530.0320.0001.0000.9970.000
quarter0.5730.0650.0530.0520.0360.0370.0000.0000.0680.0600.0480.0780.0480.0810.0520.0500.0700.0000.9971.0000.000
fiscal_year0.9290.1230.1910.1400.1130.1360.0540.1890.1410.1260.1420.0570.1150.1380.0980.1080.1820.0000.0000.0001.000

Missing values

2023-09-16T02:22:38.836812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T02:22:39.257557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idmonthfuel_type_petrolfuel_type_dieselfuel_type_electricfuel_type_othersvehicleClass_MotorCyclevehicleClass_MotorCarvehicleClass_AutoRickshawvehicleClass_AgriculturevehicleClass_othersseatCapacity_1_to_3seatCapacity_4_to_6seatCapacity_above_6brand_new_vehiclespre_owned_vehiclescategory_non_transportcategory_transportdistrictmmmquarterfiscal_year
012019-04-011791030117622153084429041278161104182717195421477198561163RangareddyAprQ12019
122019-04-01306630660299514249641283156189333322563203175KamareddyAprQ12019
232019-04-011577215001546792921117168310451751411648144Rajanna SircillaAprQ12019
342019-04-0119612812019397272481132082146162209352075169JangoanAprQ12019
452019-04-0115523090015127669109951696145201820411701160Jayashankar BhupalpallyAprQ12019
562019-04-0145506600044312641938423847144504451041064802408MahabubnagarAprQ12019
672019-04-013112496003038233707219532762795334701383353255SiddipetAprQ12019
782019-04-012200496002160110741621902483194192645512440256NagarkurnoolAprQ12019
892019-04-0117362632916781215570861806173311980301879131NirmalAprQ12019
9102019-04-012033173002017672247532111878216244213472Jogulamba GadwalAprQ12019
idmonthfuel_type_petrolfuel_type_dieselfuel_type_electricfuel_type_othersvehicleClass_MotorCyclevehicleClass_MotorCarvehicleClass_AutoRickshawvehicleClass_AgriculturevehicleClass_othersseatCapacity_1_to_3seatCapacity_4_to_6seatCapacity_above_6brand_new_vehiclespre_owned_vehiclescategory_non_transportcategory_transportdistrictmmmquarterfiscal_year
143014312023-03-0111542434411056156341491282108151348571202203Kumurambheem AsifabadMarQ42022
143114322023-03-0130444443164227916241369919630327229235822643526320NizamabadMarQ42022
143214332023-03-011455280489712552771127516114514022717581221615265MedakMarQ42022
143314342023-03-01529510044916794279167766712472448832248322664882361101361SangareddyMarQ42022
143414352023-03-012079429442144302170796724312323271862264271051228643320238962288Medchal_MalkajgiriMarQ42022
143514362023-03-01241122169260618482150157921684017582249371391093262854450274213314HyderabadMarQ42022
143614372023-03-019962862067884137751061671123219271300691129240WanaparthyMarQ42022
143714382023-03-0188618371128281461754107966167191060921030122Rajanna SircillaMarQ42022
143814392023-03-011659472399714132899518228818293914721071601889378Yadadri BhuvanagiriMarQ42022
143914402023-03-01176723932281532251655816016803444219541121845221KamareddyMarQ42022